This folder contains data from the Citizen Public Services Survey, conducted in Mexico City in May 2021.
The in-person survey was conducted using the following sampling strategy: 
  + Neighborhoods (electoral precincts, or secciones) were blocked on predicted noise-making power and vote share for the incumbent (0-36%, 36-46%, and 46+%). A precinct was considered to have predicted "noise-making power" if a) the precinct contained a public housing unit (unidad habitacional), it was in the top 25th percentile traffic-volume, or its centroid than 1.5 km from the alcaldia headquarters. These 6 blocks were all assigned equal probability in the data. 
  + Within blocks, 105 precincts were selected 
  + Within selected precincts, a block was randomly selected using excel
  + Within that block, enumerators counted the total blocks and conducted random-walk sampling to sample 6 residences. 

The online survey was not random; rather, participants were recruited using Facebook advertisements. Facebook advertisements were targeted using centroids that covered the entire span of Mexico City neighborhoods over the course of one month; however, this geographic targeting was noisy. 

Survey data has been cleaned and restricted to the colonia level in order to preserve respondents' anonymity. 

Weights are also provided in the survey data. There are two sets of weights, calculated using 2020 census data and the rake_survey() function from the pewmethods package: 
+ Sampling weights account for features of the design: for the in-person survey, the weights are the inverse proportion of the probability of selection. For the online survey, the weights are the inverse proportion of the group's representation on Facebook, calculated based on gender, age, and education level. 
+ Post-stratification weights attempt to correct for non-representativeness. These weights adjust for age, alcaldia, and education level. 


